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Aug 7, 2017 11:05 AM
(5607 views)

I am running a one-way ANOVA and am looking for a solution to match/adjust the analysis based on my demographic variables (age, weight, etc.).

Any solution to this?

Best,

JD

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The age, weight, etc. are usually considered covariates. So you usually will conduct the analysis by using Fit Model rather than Fit Y by X. You would add your primary predictor variable along with all of the other possible demographic variables into the model. There are some potential pitfalls that would be too lengthy to discuss in this forum. I would recommend looking at a linear models text. JMP also offers classes on fitting these types of models: https://support.sas.com/edu/schedules.html?ctry=us&crs=JANR

Dan Obermiller

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The age, weight, etc. are usually considered covariates. So you usually will conduct the analysis by using Fit Model rather than Fit Y by X. You would add your primary predictor variable along with all of the other possible demographic variables into the model. There are some potential pitfalls that would be too lengthy to discuss in this forum. I would recommend looking at a linear models text. JMP also offers classes on fitting these types of models: https://support.sas.com/edu/schedules.html?ctry=us&crs=JANR

Dan Obermiller

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Re: Adjustment for variable (age, weight, sex, BMI, etc.)

Dan,

Do you happen to have an output of that process? Or a step by step? Regardless of pitfalls, just trying to do a simple analysis adjusted for a few variables.

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Re: Adjustment for variable (age, weight, sex, BMI, etc.)

Look in the online JMP manual: Fitting Linear Models. Look on page 214 for the Analysis of Covariance with Unequal Slopes Example. This is not the same as your example, as your situation sounds more complex. But this will give you an idea of how to specify the model and what a small part of what the output may look like. The full output could be seen in just about any standard least squares multiple regression model that is fit in JMP.

Although your question can be simply stated, the analysis may not be so easy. If it were easy, there would be no need for the discussion here. There are many issues to consider such as: Is there collinearity in the data? Are the data complete or are there "gaps"? Are there time-related effects? Are the effects fixed or random? Should interactions be considered? The devil is always in the details. Much more information would be needed to be certain that proper advice is given. You may wish to refer to a text on linear models such as Applied Linear Statistical Models by Kutner, Neter, et. al.

Dan Obermiller

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Re: Adjustment for variable (age, weight, sex, BMI, etc.)

I am doing an age adjustment using the fitting model.

would you happen to know if I can get the means and std of the adjusted results? When I do the process you've described I can see the p value of the adjusted variables, but I don't see their distribution. I know that in SPSS this is one of the outputs.

thanks

would you happen to know if I can get the means and std of the adjusted results? When I do the process you've described I can see the p value of the adjusted variables, but I don't see their distribution. I know that in SPSS this is one of the outputs.

thanks

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Re: Adjustment for variable (age, weight, sex, BMI, etc.)

I believe the Least Square Means are what you are looking for. They are with the Leverage Plots. They would either be tot he right of the Actual by Predicted plot or you may need to request them from the red pop-up menu.

Dan Obermiller

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Re: Adjustment for variable (age, weight, sex, BMI, etc.)

Hello,

This is what I get from the leverage plot - what I think you refer to is

grayed out. Any idea?

This is what I get from the leverage plot - what I think you refer to is

grayed out. Any idea?

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Re: Adjustment for variable (age, weight, sex, BMI, etc.)

There is no attached picture, so I do not know what you are getting from the leverage plot.

Dan Obermiller

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Re: Adjustment for variable (age, weight, sex, BMI, etc.)

I would start by defining the aim of the study and the target variable (endpoint).

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Re: Adjustment for variable (age, weight, sex, BMI, etc.)

Ted,

I am looking at a genotype that has 3 subtypes (1:1, 1:2, 2:2) and observing their relationship to glucagon.

My genotype is nominal and my glucagon is continuous.

I have several other continuous variables (covariates) such as: body weight, age, and gender.

I am wanting to run an ANCOVA with glucagon as my response variable and my genotype as my factor. My covariates are as I said previously.

Does that clear it up?